
Wavelet transforms serve as an important and powerful analyzing tool for time-frequency analysis and have been applied in a number of fields including signal processing, image processing, sampling theory, differential and integral equations, quantum mechanics and medicine. However, the signal analysis capability of the wavelet transform is limited in the time-frequency plane as each wavelet component is actually a differently scaled bandpass filter in the frequency domain and hence, it does not serve as an efficient tool for processing the higher dimensional signal whose energy is not well concentrated in the frequency domain. One of the examples of such signal is chirp like signals [1]}. Therefore, in order to obtain joint signal representations in both time and frequency domains, the linear canonical wavelet transform (LCWT) has been introduced in the context of time-frequency analysis. The LCWT inherits the excellent mathematical properties of wavelet transform and linear canonical transform along with some fascinating properties of its own. In recent years, this transform has been paid a considerable amount of attention, resulting in many applications in the areas of optics, quantum mechanics, pattern recognition and signal processing [2]}, [3]}, [4]}, [5]}, [6]}.
